Metis
Co-Pilot for DB Ops - Building in Public

Co-Pilot for DB Ops - Building in Public

In Progress: My Ongoing Odyssey to Develop a Database Operations Co-Pilot

Itay Braun's photo
Itay Braun
·

8 min read

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In this series of posts I'll write about a long journey to build a Co-Pilot for DB Operations.

TLDR:

  • What is a Co-Pilot for DB Ops

  • Some examples

  • Product guidelines

Building a new AI tool: Co-Pilot of DB Ops

Think of a co-pilot as your everyday AI buddy. Developers rely on AI tools to boost their productivity. With GitHub co-pilot, you can easily discuss your codebase. Whether you're fixing bugs or brainstorming new features, just ask for help when you're stuck. And with codium.ai, you'll receive smart suggestions right within your coding environment, be it your IDE or Git platform. This helps you code smarter and feel confident about your work.

So, I wondered: what about a co-pilot for data operations? Could I create one?

I soon realized it would be a tough challenge. But I didn't anticipate just how difficult it would be, and unfortunately, the journey is still ongoing with limited success.

What is a Co-Pilot for DB Ops?

DB Ops, short for Database Operations, refers to the set of processes and activities involved in managing and maintaining databases within an organization. This includes tasks such as database design, deployment, configuration, optimization, monitoring, troubleshooting, backup, and recovery. Essentially, DB Ops ensures that databases are running smoothly, efficiently, and securely to meet the needs of the organization's data-driven applications and services.

A co-pilot for DB Ops should be equipped with functionalities tailored to streamline and enhance various aspects of managing and optimizing databases. Making the life of the DevOps / SRE / DBRE or the developer who owns the DB much easier.

  1. Performance Monitoring and Optimization: The co-pilot should continuously monitor the performance of databases, identifying bottlenecks, slow queries, or resource constraints. It should offer suggestions and insights to optimize database performance, such as index tuning, query optimization, or resource allocation adjustments.

  2. Automated Routine Tasks: Routine tasks like database backups, updates, patching, and schema changes can be automated by the co-pilot, freeing up valuable time for database administrators (DBAs) to focus on more strategic initiatives.

  3. Proactive Issue Detection and Resolution: By analyzing database metrics and logs in real-time, the co-pilot should be able to detect anomalies, errors, or potential issues early on. It should provide alerts and recommendations for proactive resolution to prevent downtime or performance degradation.

  4. Capacity Planning and Scaling: The co-pilot should leverage predictive analytics to forecast future resource requirements based on historical data and trends. This enables DBAs to plan for scaling up or down the database infrastructure proactively to accommodate growing workloads or optimize resource utilization.

  5. Security and Compliance Management: Ensuring the security and compliance of databases is paramount. The co-pilot should assist in implementing security best practices, such as access controls, encryption, and auditing, while also helping to enforce compliance with regulatory standards like GDPR or HIPAA.

  6. Resource Utilization Monitoring: The co-pilot can track resource consumption metrics such as CPU usage, memory utilization, storage capacity, and I/O throughput. By analyzing these metrics over time, it can identify underutilized resources, optimize resource allocation, and recommend rightsizing instances to minimize unnecessary costs.

  7. Performance vs. Cost Optimization: Balancing database performance with cost efficiency is crucial. The co-pilot can analyze the relationship between performance metrics (e.g., response time, throughput) and resource costs (e.g., compute instances, storage) to identify cost-effective performance optimization strategies. This may involve optimizing queries, leveraging caching mechanisms, or choosing appropriate instance types based on workload patterns.

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While the DB Ops tool can and should work with any database, I'll focus on MySQL and PostgreSQL. As the CTO of Metis I know these two (and MS SQL Server) very well, so this is a good place to start.

Product Guidelines

Guideline 1: First, Do No Harm

At the core of DB Ops co-pilot's functionality lies the principle of "first, do no harm." To uphold this principle, the co-pilot ensures that users interact with production databases with the utmost caution and responsibility. By adhering to the "first, do no harm" principle and implementing these guidelines, the DB Ops co-pilot ensures that users engage with production databases responsibly, minimizing the risk of disruptions, data loss, or security breaches.

Here's how:

  1. Minimum Privileges: Users are granted read-only access by default. Any action that could modify the database requires special permission. This minimizes the risk of unintentional changes or data corruption.

  2. Read-Only Access: Users can view database structures, query data, and analyze performance metrics without the ability to make alterations. This restriction prevents accidental modifications that could disrupt operations.

  3. Special Permission for Modifications: Any modification, whether it's altering schema, updating records, or executing administrative tasks, necessitates explicit authorization. This ensures that changes are deliberate, well-considered, and authorized by relevant stakeholders.

  4. Logging Every Operation: Every interaction with the database, including read-only queries and administrative actions, is logged meticulously. This comprehensive logging provides an audit trail for accountability, troubleshooting, and forensic analysis if issues arise.

  5. Documentation of Reversion Procedures: In the rare event of an unintended change or an operation causing unexpected consequences, documented procedures for reverting changes are readily available. These procedures outline step-by-step instructions for restoring the database to its previous state, mitigating any adverse impacts swiftly and effectively.

Guideline 2: Great Developer Experience (DX)

A paramount aspect of the DB Ops co-pilot is providing an exceptional Developer Experience (DX). Here's how we ensure it:

  1. Honesty and Transparency: If the co-pilot encounters a scenario it's not equipped to handle or lacks sufficient information, it will transparently communicate, stating "I don't know." This fosters trust and ensures that users receive accurate guidance.

  2. No Hallucination: The co-pilot never makes assumptions or guesses. It relies solely on factual data and established knowledge to provide responses. This prevents misleading information and maintains the integrity of interactions.

  3. Fast Responses: Speed is key in a dynamic development environment. The co-pilot strives to deliver swift responses to user queries and commands, minimizing waiting times and keeping productivity high.

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Google Vertex: In generative AI, grounding is the ability to connect model output to verifiable sources of information. If you provide models with access to specific data sources, then grounding tethers their output to these data and reduces the chances of inventing content. This is particularly important in situations where accuracy and reliability are significant.

Guideline 3: Compliance

Ensuring compliance with regulatory standards is paramount for the DB Ops co-pilot. Here's how it upholds compliance:

  1. Adherence to Regulations: The co-pilot strictly adheres to regulatory compliance requirements, including GDPR, HIPAA, SOC 2, and other relevant standards. This ensures that interactions with production databases comply with legal and industry-specific regulations.

  2. Support for Security Measures: To protect sensitive data, the co-pilot supports encryption, data masking, and other security features mandated by regulations. It employs robust encryption protocols to safeguard data both at rest and in transit.

  3. Data Privacy: The co-pilot prioritizes data privacy by limiting access to sensitive information and enforcing access controls based on user roles and permissions. It ensures that only authorized personnel can access, view, or modify confidential data.

  4. Auditing and Reporting: Comprehensive auditing and reporting capabilities are integral to compliance. The co-pilot logs all interactions and activities, providing audit trails for regulatory audits and compliance assessments. This transparency helps demonstrate adherence to regulatory requirements.

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The immediate solution involves exploring a local Language Model (LLM) with a modest size (4-8GB) to ensure data remains within the organization and avoid ongoing costs. However, the challenge lies in maintaining good results and performance with smaller models. Strategies like optimization, fine-tuning, feature engineering, ensemble learning, and hardware acceleration can help overcome this challenge and ensure satisfactory performance without compromising data security. I plan investigating this direction.

Guideline 4: Cost-Effectiveness

The DB Ops co-pilot must prioritize cost-effectiveness, ensuring that the value it provides outweighs the associated expenses.

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The pricing considerations necessitate that the solution cannot generate extensive prompts encompassing all schema details. Doing so would prove too costly, slow down operations significantly, and encounter limitations from APIs constrained by the size of the context window.

Next Posts:

  • Why building in public?

  • Deep dive to the user stories, what exactly this tool must be able to do and what is nice to have. A long list of examples.

  • Architecture: Agents vs copying the data to a local DB, such as DuckDB.

  • Testing the OpenAI API. Spoiler alert: the results aren't promising.

  • First PoC using a no code chatbot (chatbase). Spoiler alert - The PoC proves it is possible.

  • First PoC on a local LLM. Spoiler Alert - Horrible results.

  • How can text2SQL tools help? understanding text2sql.

  • Training a model using vanna.ai

  • Trying a solution based on the Postgres extension. This can simplify the implementation.

  • Fine Tuning the model

Tools and technologies:

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This list is extensive. I've tested some of the technologies, while others are on my testing agenda for the near future. Keeping track of all the announcements and pertinent solutions can be challenging and documenting them also requires time and effort.

No code chatbot:

Chatbot dev frameworks (UI):

Text2SQL

LLM using Postgres

  • pg_vectorize: A Postgres extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs. Here I wrote about it. I might want to test mindsdb too.

Local LLM (don't worry about cost and data security)

  • GPT4All - A free-to-use, locally running, privacy-aware chatbot. No GPU or internet required.

Inspiration from Other Co-Pilots

db-ops